Aim: The aim of the research work is to detect image imitation using a support vector machine using repositions data. Materials and Methods: The categorizing is performed by adopting a sample size of n = 10 in Support Vector Machine and sample size n = 10 in Random Forest algorithms with a sample size = 2, G power of 80%. Results: The analysis of the results shows that the Support Vector Machine has a high accuracy of (95.878) in comparison with the Random Forest algorithm (91.584). There is a statistically insignificant difference between the study groups with significance value p= 0.918 (p>0.05). Conclusion: Prediction in detection of Figure Imitation shows that the Support Vector Machine appears to generate better accuracy than the Figure Imitation Random Forest algorithm.